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Code Huddle
Specialized service

AI Agent Development Company

Code Huddle builds custom AI agents and copilots for product and operations teams. Connect a model to the tools your workflow needs, define what it can do, and keep consequential actions under human control. Start with one task and a measurable definition of success.

Custom AI AgentsAI CopilotsTool IntegrationHuman Approval
Discuss Your AI WorkflowSee related work

Capability map

What we design, build, and improve

An agent needs more than a prompt. It needs a bounded task, reliable tools, appropriate access, and a way to recover when a step fails. We scope those boundaries with your team before choosing a model or framework. Our related AI work includes LLM-assisted industrial data enrichment for Flux Foundry and AI-assisted buyer–seller matching for House Hint. Those projects demonstrate relevant data and product engineering; your agent is evaluated against its own workflow and acceptance criteria.

01

Workflow discovery and automation fit

Map the inputs, decisions, systems, and exception paths. Use ordinary automation where rules are predictable, and introduce model-driven decisions where interpretation is needed. Define the task the agent must complete and when it must stop.

02

Tools connected to your product

Integrate approved APIs, databases, and internal services. Validate tool inputs and outputs, use scoped credentials, and separate reading information from changing records or sending requests.

03

Knowledge retrieval with access controls

Retrieve relevant documents within the user’s permissions. Preserve source references, define how stale information is handled, and agree what data may reach each model provider.

04

Copilots and approval steps

Give users a reviewable recommendation or action preview. Add explicit approval for selected operations, a clear cancellation path, and escalation when information is incomplete or the task is outside scope.

05

Evaluation before release

Build representative examples and failure cases around the real workflow. Measure task completion, incorrect actions, latency, and cost, then compare the agent with a simpler baseline before extending its autonomy.

06

Monitoring and operational handover

Record tool calls and outcomes with appropriate redaction. Set execution limits, retries, and alerts; document how to pause a workflow, investigate a failed run, and change a model safely.

Delivery model

A visible path from uncertainty to production

  1. 01

    Define one workflow

    Review sample tasks, available APIs, data permissions, and the current manual process. Agree the success metric, approval requirements, and failure conditions before estimating the build.

  2. 02

    Build and evaluate

    Implement a limited workflow with a reviewable interface and traceable tool calls. Test realistic requests, missing data, tool failures, and attempts to exceed the allowed scope.

  3. 03

    Release with controls

    Roll out to an agreed user group, monitor quality and operating cost, and document recovery. Expand the workflow only after the initial acceptance criteria are met.

Commercial model

Scope and cost follow uncertainty

Defined outcomes can use milestones. Evolving products are usually better served by transparent team capacity. Estimates follow discovery of workflows, integrations, constraints, and acceptance criteria.

Engineering judgment

Tradeoffs stay explicit

Build versus buy, delivery speed, operating cost, security, maintainability, migration, and technical ambition are discussed as product decisions—not hidden implementation details.

Where this fits

Common product situations

  1. 01An operations copilot that prepares a record update for approval
  2. 02A research assistant that retrieves and compares permitted sources
  3. 03Document intake with extraction, validation, and exception review
  4. 04Support triage that proposes the next action for a team member
  5. 05A product assistant that coordinates steps across approved APIs

Technology choices

Selected for the system—not the trend

The final stack follows product constraints, team capability, integration boundaries, security, scale, and long-term ownership.

PythonTypeScriptOpenAI APIAnthropic ClaudeLangGraphPostgreSQLpgvectorREST APIsAWS

Evidence related to AI Agent Development

Explore product stories with related architecture, workflows, and delivery decisions.

Ensemble – Flux FoundryLLM-assisted data enrichment, traceable processing, and recommendations for engineer review.House HintAI-assisted matching integrated into a real estate product.

Questions before starting

The details buyers usually need

A chatbot is a conversational interface. An agent can select and use tools to work toward a task, such as retrieving records and preparing an update. A chatbot may contain agent capabilities, but many question-answering products only need retrieval and a response. We establish which behavior your workflow needs before building.

Yes. A copilot can gather context, suggest a next step, and prepare changes for a person to approve. This is useful when users need assistance while retaining control over actions. The scope defines which steps can run automatically and which require review.

It can use systems that expose suitable APIs or another approved integration path. We review access, rate limits, data quality, and write permissions first. The implementation should use scoped credentials and preserve each user’s access rules rather than giving a model unrestricted access.

The estimate depends on the workflow, number of integrations, data preparation, evaluation, interface, and deployment requirements. Model usage and hosting are recurring costs separate from development. Share sample tasks and the systems involved so we can prepare a scope and estimate.

A contained workflow and an agent spanning several systems have different delivery needs. We estimate after reviewing API access, data readiness, approval steps, and acceptance criteria, then agree milestones for discovery, evaluation, and release. We do not promise one delivery window for every agent.

We test agreed tasks and failure cases, inspect tool calls, and measure completion, incorrect actions, latency, and operating cost. Release criteria also include access controls, execution limits, approval flows, monitoring, and a documented way to pause or recover the workflow.

Plan your project

Practical questions and scope decisions before you commission a build.

Agentic AI vs Generative AI: Which Does Your Product Need?Compare agentic AI and generative AI through practical workflows, tool access, human approval, cost, and evaluation before choosing what to build.

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